Most teams building self improving agents follow a similar trajectory: the agent runs, reflects, updates an artifact, and the metric rises. In production, the metric can still rise while a later incident traces back to a learned change that passed review but was evaluated against the wrong signals.

This talk examines different self learning systems that may change the agent context, the agent harness, or model weights. Each offers different gains and failure modes. Memory can absorb sycophancy and inflate its reward signal. Skill libraries can overfit to the last task or be tainted by one bad run. Harness changes can discard useful behaviour as new tools arrive. Weight updates persist longest and are the hardest to reverse.

I’ll cover the controls that make self iteration viable in production: bounded, reversible updates; provenance for every learned artifact; promotion gates; and evaluations that separate improvement from noise.